Lifelong Machine Learning Architecture for Classification Article Swipe
Xianbin Hong
,
Sheng-Uei Guan
,
Ka Lok Man
,
Prudence W. H. Wong
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/sym12050852
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/sym12050852
Benefiting from the rapid development of big data and high-performance computing, more data is available and more tasks could be solved by machine learning now. Even so, it is still difficult to maximum the power of big data due to each dataset is isolated with others. Although open source datasets are available, algorithms’ performance is asymmetric with the data volume. Hence, the AI community wishes to raise a symmetric continuous learning architecture which can automatically learn and adapt to different tasks. Such a learning architecture also is commonly called as lifelong machine learning (LML). This learning paradigm could manage the learning process and accumulate meta-knowledge by itself during learning different tasks. The meta-knowledge is shared among all tasks symmetrically to help them to improve performance. With the growth of meta-knowledge, the performance of each task is expected to be better and better. In order to demonstrate the application of lifelong machine learning, this paper proposed a novel and symmetric lifelong learning approach for sentiment classification as an example to show how it adapts different domains and keeps efficiency meanwhile.
Related Topics
Concepts
Computer science
Lifelong learning
Machine learning
Artificial intelligence
Task (project management)
Process (computing)
Meta learning (computer science)
Multi-task learning
Instance-based learning
Architecture
Inductive transfer
Online machine learning
Big data
Incremental learning
Semi-supervised learning
Robot learning
Data mining
Visual arts
Art
Psychology
Economics
Operating system
Pedagogy
Robot
Mobile robot
Management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym12050852
- https://www.mdpi.com/2073-8994/12/5/852/pdf?version=1590399195
- OA Status
- gold
- Cited By
- 16
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3026935788
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3026935788Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/sym12050852Digital Object Identifier
- Title
-
Lifelong Machine Learning Architecture for ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-22Full publication date if available
- Authors
-
Xianbin Hong, Sheng-Uei Guan, Ka Lok Man, Prudence W. H. WongList of authors in order
- Landing page
-
https://doi.org/10.3390/sym12050852Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-8994/12/5/852/pdf?version=1590399195Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2073-8994/12/5/852/pdf?version=1590399195Direct OA link when available
- Concepts
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Computer science, Lifelong learning, Machine learning, Artificial intelligence, Task (project management), Process (computing), Meta learning (computer science), Multi-task learning, Instance-based learning, Architecture, Inductive transfer, Online machine learning, Big data, Incremental learning, Semi-supervised learning, Robot learning, Data mining, Visual arts, Art, Psychology, Economics, Operating system, Pedagogy, Robot, Mobile robot, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4, 2023: 4, 2022: 3, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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